Machine Translation Vector Space Disambiguation
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Solution Overview
Problem
Current machine translation systems face limitations in handling word sense ambiguity, idiomatic expressions, anaphora resolution, and logical decomposition, resulting in poor fidelity and accuracy in translating text between languages.
Innovation Solution
The use of a conceptual representation space, such as a Latent Semantic Indexing (LSI) space, to generate vector representations of terms and documents, allowing for improved translation by disambiguating words, handling idiomatic expressions, and decomposing complex sentences, through similarity-based translations and dictionary-level or word-level disambiguation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If rule-based systems or statistical machine translation are used, then automation is achieved, but translation fidelity and accuracy deteriorate due to inability to handle word sense ambiguity, idiomatic expressions, and complex sentence structures
Solution Approach 1:
The patent introduces a conceptual representation space as an intermediary layer between source and target languages. This vector space representation serves as a mediator that captures semantic relationships and contextual meanings, enabling the system to resolve word sense ambiguity and handle idiomatic expressions while maintaining automation. The conceptual space acts as a bridge that preserves translation fidelity without requiring manual intervention.
2Productivity
If conventional machine translation approaches are used, then translation speed is maintained, but handling of complex sentences and idiomatic expressions deteriorates
Solution Approach 1:
The patent segments complex sentences into smaller conceptual units within the vector space representation. By decomposing sentences into constituent concepts and representing them as vectors, the system can process complex structures systematically while maintaining overall translation coherence. This segmentation approach enables reliable handling of complex sentences without sacrificing translation speed, as the vector operations can be performed efficiently in parallel.
Data Source
AI summary
An embodiment of the present invention provides a method for automatically translating text. First, a conceptual representation space is generated based on source-language documents and target-language documents, wherein respective terms from the source-language and target-language documents have a representation in the conceptual representation space. Second, a new source-language document is represented in the conceptual representation space, wherein a subset of terms in the new source-language document is represented in the conceptual representation space, such that each term in the subset has a representation in the conceptual representation space. Then, a term in the new source-language document is automatically translated into a corresponding target-language term based on a similarity between the representation of the term and the representation of the corresponding target-language term.